Estimation of Diabetes in a High-Risk Adult Chinese Population Using J48 Decision Tree Model

References

• Global Burden of Metabolic Risk Factors for Chronic Diseases Collaboration. Cardiovascular disease, chronic kidney disease, and diabetes mortality burden of cardiometabolic risk factors from 1980 to 2010: a comparative risk assessment. Lancet Diabetes Endocrinol. 2014;2(8):634–647. doi:10.1016/S2213-8587(14)70102-0
   PubMed Web of Science ®Google Scholar
• Seuring T, Archangelidi O, Suhrcke M. The economic costs of type 2 diabetes: a global systematic review. Pharmacoeconomics. 2015;33(8):811–831. doi:10.1007/s40273-015-0268-9
   PubMed Web of Science ®Google Scholar
• Organization WH. Global report on diabetes 2016. http://apps.who.int/iris/bitstream/10665/204871/1/9789241565257_eng.pdf. Accessed November 2, 2020.
   Google Scholar
• Hossain P, Kawar B, El Nahas M. Obesity and diabetes in the developing world–a growing challenge. N Engl J Med. 2007;356(3):213–215. doi:10.1056/NEJMp068177
   PubMed Web of Science ®Google Scholar
• Groop L. Genetics and neonatal diabetes: towards precision medicine. Lancet. 2015;386(9997):934–935. doi:10.1016/S0140-6736(15)61428-3
   PubMedGoogle Scholar
• Hadaegh F, Bozorgmanesh MR, Ghasemi A, Harati H, Saadat N, Azizi F. High prevalence of undiagnosed diabetes and abnormal glucose tolerance in the Iranian urban population: tehran lipid and glucose study. BMC Public Health. 2008;8:176. doi:10.1186/1471-2458-8-176
   PubMed Web of Science ®Google Scholar
• Jahani M, Mahdavi M. Comparison of predictive models for the early diagnosis of diabetes. Healthc Inform Res. 2016;22(2):95–100. doi:10.4258/hir.2016.22.2.95
   PubMedGoogle Scholar
• Lélis VM, Guzmán E, Belmonte MV. A statistical classifier to support diagnose meningitis in less developed areas of Brazil. J Med Syst. 2017;41(9):145. doi:10.1007/s10916-017-0785-5
   PubMedGoogle Scholar
• Federation ID. IDF diabetes atlas seventh edition. http://www.diabetesatlas.org/across-the-globe.html.,2015.
   Google Scholar
• Organization WH 2008–2013 action plan for the global strategy for the prevention and control of non-communicable disease. https://apps.who.int/iris/bitstream/handle/10665/94384/9789241506236_eng.pdf;jsessionid=AAACAA9C0607BDB7BBA4BB517037C94C?sequence=1. Accessed November 2, 2020.
   Google Scholar
• Sicree RA, Zimmet PZ, King HO, Coventry JS. Plasma insulin response among nauruans. Prediction of deterioration in glucose tolerance over 6 yr. Diabetes. 1987;36(2):179–186. doi:10.2337/diab.36.2.179
   PubMedGoogle Scholar
• NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants. Lancet. 2016;387(10027):1513–1530. doi:10.1016/S0140-6736(16)00618-8
   PubMed Web of Science ®Google Scholar
• Ekins S, Clark AM, Dole K, et al. Data mining and computational modeling of high-throughput screening datasets. Methods Mol Biol. 2018;1755:197–221.
   PubMedGoogle Scholar
• Sumathi A, Meganathan S. Semi supervised data mining model for the prognosis of pre-diabetic conditions in type 2 Diabetes Mellitus. Bioinformation. 2019;15(12):875–882. doi:10.6026/97320630015875
   PubMedGoogle Scholar
• Pei D, Zhang C, Quan Y, Guo Q. Identification of potential type ii diabetes in a chinese population with a sensitive decision tree approach. J Diabetes Res. 2019;2019:4248218. doi:10.1155/2019/4248218
   PubMedGoogle Scholar
• Fernández L, Mediano P, García R, Rodríguez JM, Risk Factors MM. Predicting infectious lactational mastitis: decision tree approach versus logistic regression analysis. Matern Child Health J. 2016;20(9):1895–1903. doi:10.1007/s10995-016-2000-6
   PubMed Web of Science ®Google Scholar
• Choi SB, Kim WJ, Yoo TK, et al. Screening for prediabetes using machine learning models. Comput Math Methods Med. 2014;2014:618976. doi:10.1155/2014/618976
   PubMed Web of Science ®Google Scholar
• Meng XH, Huang YX, Rao DP, Zhang Q, Liu Q. Comparison of three data mining models for predicting diabetes or prediabetes by risk factors. Kaohsiung J Med Sci. 2013;29(2):93–99. doi:10.1016/j.kjms.2012.08.016
   PubMed Web of Science ®Google Scholar
• Worachartcheewan A, Nantasenamat C, Isarankura-Na-Ayudhya C, Pidetcha P, Prachayasittikul V. Identification of metabolic syndrome using decision tree analysis. Diabetes Res Clin Pract. 2010;90(1):e15–18. doi:10.1016/j.diabres.2010.06.009
   PubMedGoogle Scholar
• Al-Turaiki I, Alshahrani M, Almutairi T. Building predictive models for MERS-CoV infections using data mining techniques. J Infect Public Health. 2016;9(6):744–748. doi:10.1016/j.jiph.2016.09.007
   PubMedGoogle Scholar
• Ramezankhani A, Pournik O, Shahrabi J, Khalili D, Azizi F, Hadaegh F. Applying decision tree for identification of a low risk population for type 2 diabetes. Tehran lipid and glucose study. Diabetes Res Clin Pract. 2014;105(3):391–398. doi:10.1016/j.diabres.2014.07.003
   PubMed Web of Science ®Google Scholar
• Samanta B, Bird GL, Kuijpers M, et al. Prediction of periventricular leukomalacia. Part I: selection of hemodynamic features using logistic regression and decision tree algorithms. Artif Intell Med. 2009;46(3):201–215. doi:10.1016/j.artmed.2008.12.005
   PubMedGoogle Scholar
• Carbone S, Del Buono MG, Ozemek C, Lavie CJ. Obesity, risk of diabetes and role of physical activity, exercise training and cardiorespiratory fitness. Prog Cardiovasc Dis. 2019;62(4):327–333. doi:10.1016/j.pcad.2019.08.004
   PubMed Web of Science ®Google Scholar
• Weickert MO, Pfeiffer A. Impact of dietary fiber consumption on insulin resistance and the prevention of type 2 diabetes. J Nutr. 2018;148(1):7–12. doi:10.1093/jn/nxx008
   PubMed Web of Science ®Google Scholar
• Kim JH, Noh J, Choi JW, Park EC. Association of education and smoking status on risk of diabetes mellitus: a population-based nationwide cross-sectional study. Int J Environ Res Public Health. 2017;14:6. doi:10.3390/ijerph14060655
   Google Scholar
• Li G, Zhang P, Wang J, et al. The long-term effect of lifestyle interventions to prevent diabetes in the china da qing diabetes prevention study: a 20-year follow-up study. Lancet. 2008;371(9626):1783–1789. doi:10.1016/S0140-6736(08)60766-7
   PubMed Web of Science ®Google Scholar
• Saaristo T, Moilanen L, Korpi-Hyövälti E, et al. Lifestyle intervention for prevention of type 2 diabetes in primary health care: one-year follow-up of the finnish national diabetes prevention program (FIN-D2D). Diabetes Care. 2010;33(10):2146–2151. doi:10.2337/dc10-0410
   PubMed Web of Science ®Google Scholar
• Tuomilehto J, Lindström J, Eriksson JG, et al. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med. 2001;344(18):1343–1350. doi:10.1056/NEJM200105033441801
   PubMed Web of Science ®Google Scholar
• Anderson AE, Kerr WT, Thames A, Li T, Xiao J, Cohen MS. Electronic health record phenotyping improves detection and screening of type 2 diabetes in the general United States population: A cross-sectional, unselected, retrospective study. J Biomed Inform. 2016;60:162–168. doi:10.1016/j.jbi.2015.12.006
   PubMedGoogle Scholar